This statistic shows the biggest cities in Pakistan as of 2023. In 2023, approximately 18.87 million people lived in Karāchi, making it the biggest city in Pakistan.

Major Cities in Pakistan by Population

Population in largest city in Pakistan was reported at 17236230 in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. Pakistan - Population in largest city - actual values, historical data, forecasts and projections were sourced from the World Bank on March of 2025.

Pakistan PK: Population in Largest City data was reported at 15,020,931.000 Person in 2017. This records an increase from the previous number of 14,650,981.000 Person for 2016. Pakistan PK: Population in Largest City data is updated yearly, averaging 6,793,799.000 Person from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 15,020,931.000 Person in 2017 and a record low of 1,853,325.000 Person in 1960. Pakistan PK: Population in Largest City data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Pakistan – Table PK.World Bank.WDI: Population and Urbanization Statistics. Population in largest city is the urban population living in the country's largest metropolitan area.; ; United Nations, World Urbanization Prospects.; ;

Pakistan PK: Population in Largest City: as % of Urban Population data was reported at 20.922 % in 2017. This records a decrease from the previous number of 20.928 % for 2016. Pakistan PK: Population in Largest City: as % of Urban Population data is updated yearly, averaging 21.610 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 23.038 % in 1980 and a record low of 18.670 % in 1960. Pakistan PK: Population in Largest City: as % of Urban Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Pakistan – Table PK.World Bank.WDI: Population and Urbanization Statistics. Population in largest city is the percentage of a country's urban population living in that country's largest metropolitan area.; ; United Nations, World Urbanization Prospects.; Weighted average;

Graph and download economic data for Geographical Outreach: Number of Automated Teller Machines (ATMs) in 3 Largest Cities for Pakistan (PAKFCACLNUM) from 2004 to 2015 about ATM, Pakistan, banks, and depository institutions.

Major Cities Population

Accessibility to major cities dataset is modelled as raster-based travel time/cost analysis, computed for the 26 largest cities (>250k habitants) in the country. The following cities are included: City - Population Chiniot 278,528 Nawabshah 279,338 Mingora 331,377 Okara 358,146 Kasur 358,296 Mardan 359,024 Wah Cantonment 379,534 Sahiwal 388,795 Gujrat 390,758 Dera Ghazi Khan 397,362 Rahimyar Khan 420,963 Sheikhūpura 473,269 Larkana 488,006 Sukkur 500,401 Sialkot 656,730 Sargodha 658,208 Bahawalpur 762,774 Quetta 999,385 Hyderabad 1,733,622 Multan 1,872,641 Peshawar 1,969,823 Gujranwala 2,028,421 Rawalpindi Islamabad 3,106,827 Faisalabad 3,210,158 Lahore 11,119,985 Karachi 14,884,402 This 500m resolution raster dataset is part of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) aimed at the identification of value chain infrastructure sites (or optimal location).

The regional cities accessibility dataset is modelled as raster-based travel time/cost analysis. Individual cumulative travel time/cost maps were produced for major regional cities (>850k habitants) less than 500 km from the border. The following values were assumed: City - Country Delhi 16,349,831 Ahmedabad 5,633,927 Surat 4,591,246 Kabul 4,434,550 Jaipur 3,046,163 Vadodara 1,752,371 Ludhiana 1,618,879 Meerut 1,571,434 Jammu 1,529,958 Rajkot 1,390,640 Srinagar 1,264,202 Amritsar 1,183,549 Jodhpur 1,138,300 Chandigarh 1,026,459 Kota 1,001,694 Moradabad 889,810 Aligarh 874,408 Dushanbe 863,400 This 500m resolution raster dataset is part of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) aimed at the identification of value chain infrastructure sites (optimal location).

In 2023, the share of urban population in Pakistan remained nearly unchanged at around 38.04 percent. Still, the share reached its highest value in the observed period in 2023. The urban population refers to the share of the total population living in urban centers. Each country has their own definition of what constitutes an urban center (based on population size, area, or space between dwellings, among others), therefore international comparisons may be inconsistent.

Administrative districts within each province of Pakistan, shown with main cities.

Abstract

The Pakistan Social and Living Standards Measurement Survey (PSLM) 2005-06 is aimed to provide detailed outcome indicators on Education, Health, Population Welfare, Water & Sanitation and Income & Expenditure. The data provided by this survey is used by the government in formulating the policies in social sector initiated under Poverty Reduction Strategy Paper (PRSP) and Medium Term Development Framework (MTDF) in the overall context of MDGs.

Geographic coverage

National Coverage

Analysis unit

Households and Individuals.

Universe

The universe of this survey consists of all urban and rural areas of the four provinces and Islamabad excluding military restricted areas

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling Frame:

The Federal Bureau of Statistics (FBS) has developed its own urban area frame, which was up-dated in 2003. Each city/town has been divided into enumeration blocks consisting of 200- 250 households identifiable through sketch map. Each enumeration block has been classified into three categories of income groups i.e. low, middle and high keeping in view the living standard of the majority of the people. List of villages published by Population Census Organization obtained as a consequence of Population Census 1998 has been taken as rural frame.

Stratification Plan:

A. Urban Domain: Islamabad, Lahore, Gujranwala, Faisalabad, Rawalpindi, Multan, Bahawalpur, Sargodha, Sialkot, Karachi, Hyderabad, Sukkur, Peshawar and Quetta, have been considered as large sized cities. Each of these cities constitute a separate stratum and has further been sub-stratified according to low, middle and high-income groups. After excluding population of large sized city (s), the remaining urban population in each defunct Division in all the provinces has been grouped together to form a stratum.

B. Rural Domain: Each district in the Punjab, Sindh and NWFP provinces has been grouped together to constitute a stratum. Whereas defunct administrative Division has been treated as stratum in Balochistan province.

Sample Size and Its Allocation: Keeping in view the objectives of the survey the sample size for the four provinces has been fixed at 15453 households comprising 1109 sample village/ enumeration blocks, which is expected to produce reliable results.

Sample Design: A two-stage stratified sample design has been adopted in this survey.

Selection of Primary Sampling Units (PSUs): Villages and enumeration blocks in urban and rural areas respectively have been taken as Primary Sampling Units (PSUs). Sample PSUs have been selected from strata/sub-strata with PPS method of sampling technique.

Selection of Secondary Sampling Units (SSUs): Households within sample PSUs have been taken as Secondary Sampling Units (SSUs). A specified number of households i.e. 16 and 12 from each sample PSU of rural & urban area have been selected respectively using systematic sampling technique with a random start.

Mode of data collection

Face-to-face [f2f]

Research instrument

At both individual and household level, the PSLM Survey collects information on a wide range of topics using an integrated questionnaire. The questionnaire comprises a number of different sections, each of which looks at a particular aspect of household behavior or welfare. Data collected under Round II include education, diarrhea, immunization, reproductive health, pregnancy history, maternity history, family planning, pre and post-natal care and access to basic services.

Cleaning operations

Data quality in PSLM Survey has been ensured through built in system of checking of field work by the supervisors in the field as well as teams from the headquarters. Regional/ Field offices ensured the data quality through preliminary editing at their office level. The entire data entry was carried at the FBS headquarter Islamabad and the data entry programme used had a number of in built consistency checks.

Data appraisal

To determine the reliability of the estimates, Coefficient of Variation (CV’s) and confidence Limit of important key indicators have been worked out and are attached as Appendix - C of the survey report (provided under Related Materials).

Abstract

The Pakistan Social and Living Standards Measurement (PSLM) Survey is one of the main mechanisms for monitoring the implementation of the Poverty Reduction Strategy Paper (PRSP). It provides a set of representative, population-based estimates of social indicators and their progress under the PRSP. These include intermediate as well as 'output' measures, which assess what is being provided by the social sectors - enrolment rates in education, for example. They include a range of 'outcome' measures, which assess the welfare of the population - Immunisation Rate, for example.

An important objective of the PSLM Survey is to try to establish what the distributional impact of PRSP has been. Policymakers need to know, for example, whether the poor have benefited from the programme or whether increased government expenditure on the social sectors has been captured by the better off.

Geographic coverage

National, excluding military restricted areas.

Analysis unit

• Individual
• Household

Universe

The universe of this survey consists of all urban and rural areas of the four provinces and Islamabad excluding military restricted areas.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling Frame: Pakistan Bureau of Statistics (PBS) has developed its own area sampling frame for both Urban and Rural domains. Each city/town is divided into enumeration blocks. Each enumeration block is comprised of 200 to 250 households on the average with well-defined boundaries and maps. The list of enumeration blocks are updated from field on the prescribed proforma by Quick Count technique for urban domain in 2013 and the updated list of villages/mouzas/dehs or its part (block), based on House Listing 2011 for conduct of Population Census are taken as sampling frames. Enumeration blocks are considered as Primary Sampling Units (PSUs) for urban and rural domains respectively.

Stratification Plan Urban Domain: Large cities Karachi, Lahore, Gujranwala, Faisalabad, Rawalpindi, Multan, Sialkot, Sargodha, Bahawalpur, Hyderabad, Sukkur, Peshawar, Quetta and Islamabad are considered as large cities. Each of these cities constitutes a separate stratum, further substratified according to low, middle and high income groups based on the information collected in respect of each enumeration block at the time of demarcation/ updating of urban area sampling frame. Remaining Urban Areas: In all the four provinces after excluding the population of large cities from the population of an administrative division, the remaining urban population is grouped together to form a stratum. Rural Domain: Each administrative district for all four provinces namely Punjab, Sindh, Khyber Pakhtunkhwa (KP) and Balochistan is considered as an independent stratum.

Selection of primary sampling units (PSUs): Enumeration blocks in both Urban and rural domains are taken as Primary Sampling Units (PSUs). Sample PSUs from each ultimate stratum/sub-stratum are selected with probability proportional to size (PPS) method of sampling scheme. In both Urban and Rural domains, the number of households in an enumeration block is considered as measure of size.

Selection of secondary sampling units (SSUs): The listed households of sample PSUs are taken as Secondary Sampling Units (SSUs). A specified number of households i.e. 12 from each urban sample PSU and 16 from rural sample PSU are selected with equal probability using systematic sampling technique with a random start.

Sample Size and its Allocation: Keeping in view the objectives of the survey, the sample size for the four provinces has been fixed at 5428 sample blocks (PSU’s) comprising 81,992 households (SSU’s), which is expected to produce reliable results at the district level.

Detailed sampling plan is attached as Appendix A of the survey report.

Sampling deviation

It is worth mentioning here that Panjgur district of Balochistan was dropped from the scope of the survey at the allocation stage due to prevailing situation in Panjgur district. While 7 PSUs from Sindh, 13 PSUs from KP and 82 PSUs from Balochistan province ( including Kech district) were dropped from the scope of the survey during execution of the survey due to law and order situation.

Mode of data collection

Face-to-face [f2f]

Research instrument

At both individual and household level, the PSLM Survey collects information on a wide range of topics using an integrated questionnaire. The questionnaire comprises a number of different sections, each of which looks at a particular aspect of household behavior or welfare. Data collected under Round X includes Education, Health, Water & Sanitation and Household Economic Situation & Satisfaction by facilities and services use.

Cleaning operations

Data quality in PSLM Survey has been ensured through built in system of checking of fieldwork by the supervisors in the field as well as teams from the headquarters. Regional/ Field offices ensured the data quality through preliminary editing at their office level. The entire data entry was carried out at the PBS headquarter Islamabad and the data entry programme used had a number of in built consistency checks.

The literacy rate measures the percentage of people aged 15 and above who are able to read and write. In 2019, Pakistan's total literacy rate was around 58 percent, with less than 46.49 percent of women being literate and more than 69.29 percent of men.

Women in Pakistan need education

In Pakistan, women’s education is in dire need of improvement, and so far, the number of illiterate women has not decreased - on the contrary, it has been going up for years. Although education for both genders is not prohibited in Pakistan, women are generally not as well educated as men. But it doesn’t stop there: Pakistan is one of the countries deemed worst for women in general when it comes to quality of life and safety.

Economy and education

Pakistan is a predominantly Muslim country with a low urbanization rate, meaning the majority of its population live in rural areas, where education is traditionally harder to come by than in cities. Pakistan is still a developing country, and typically, most of the inhabitants work in the primary sector, since Pakistan is rich in arable land. However, the tertiary sector generates the lion’s share of GDP. If the country wants to make the leap to being a developed nation, education and equality need to be higher on the list.

The number of billionaires in Pakistan is forecast to reach four in 2026. In 2016, there were just three individuals whose net worth exceeded one billion U.S. dollars in Pakistan.

Leading billionaire cities

According to the Hurun Global Rich List 2022, Beijing had the most billionaires in 2022. In total, 144 billionaires lived in China's capital. Furthermore, 121 billionaires resided in Shanghai, while 110 were in New York. Many of the world's billionaires are concentrated in a few megacities. A look at the primary industries of billionaires globally helps to explain the importance of traditional global business capitals such as New York, London, and Hong Kong. The inclusion of Chinese cities on the list can be explained partly by the country's industrial conglomerates' strong performance in recent years.

The effect of COVID-19 on the wealth of billionaires

Elon Musk was the billionaire whose fortune grew the most due to the COVID-19 pandemic. From September 2019 to September 2022, Elon Musk increased his net worth by 231.1 billion US dollars. Google’s Larry Page added the second highest value to his net worth during the period under consideration, with an increase of 37.5 billion dollars. In contrast, Facebook founder Mark Zuckerberg’s net worth decreased by nearly 12 billion US dollars during the same time.

Abstract

The Pakistan Integrated Household Survey (PIHS) was conducted jointly by the Federal Bureau of Statistics (FBS), Government of Pakistan, and the World Bank. The survey was part of the Living Standards Measurement Study (LSMS) household surveys that have been conducted in a number of developing countries with the assistance of the World Bank. The purpose of these surveys is to provide policy makers and researchers with individual, household, and community level data needed to analyze the impact of policy initiatives on living standards of households.

The Pakistan Integrated Household Survey was carried out in 1991. This nationwide survey gathered individual and household level data using a multi-purpose household questionnaire. Topics covered included housing conditions, education, health, employment characteristics, selfemployment activities, consumption, migration, fertility, credit and savings, and household energy consumption. Community level and price data were also collected during the course of the survey.

Geographic coverage

National

Analysis unit

• Households
• Individuals
• Communities

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for the PIHS was drawn using a multi-stage stratified sampling procedure from the Master Sample Frame developed by FBS based on the 1981 Population Census.

SAMPLE FRAME:

This sample frame covers all four provinces (Punjab, Sindh, NWFP, and Balochistan) and both urban and rural areas. Excluded, however, are the Federally Administered Tribal Areas, military restricted areas, the districts of Kohistan, Chitral and Malakand and protected areas of NWFP. According to the FBS, the population of the excluded areas amounts to about 4 percent of the total population of Pakistan. Also excluded are households which depend entirely on charity for their living.

The sample frame consists of three main domains: (a) the self-representing cities; (b) other urban areas; and (c) rural areas. These domains are further split up into a number of smaller strata based on the system used by the Government to divide the country into administrative units. The four provinces of Pakistan mentioned above are divided into 20 divisions altogether; each of these divisions in turn is then further split into several districts. The system used to divide the sample frame into the three domains and the various strata is as follows: (a) Self-representing cities: All cities with a population of 500,000 or more are classified as self-representing cities. These include Karachi, Lahore, Gujranwala, Faisalabad, Rawalpindi, Multan, Hyderabad and Peshawar. In addition to these cities, Islamabad and Quetta are also included in this group as a result of being the national and provincial capitals respectively. Each self-representing city is considered as a separate stratum, and is further sub-stratified into low, medium, and high income groups on the basis of information collected at the time of demarcation or updating of the urban area sample frame. (b) Other urban areas: All settlements with a population of 5,000 or more at the time of the 1981 Population Census are included in this group (excluding the self-representing cities mentioned above). Urban areas in each division of the four provinces are considered to be separate strata. (c) Rural areas: Villages and communities with population less than 5,000 (at the time of the Census) are classified as rural areas. Settlements within each district of the country are considered to be separate strata with the exception of Balochistan province where, as a result of the relatively sparse population of the districts, each division instead is taken to be a stratum.

Main strata of the Master Sample frame

Domain / Punjab / Sindh / NWFP / Balochistan / PAKISTAN Self-representing cities / 6 / 2 / 1 / 1 / 10 Other urban areas / 8 / 3 / 5 / 4 / 20 Rural areas / 30 / 14 / 10 / 4 / 58 Total 44 / 19 / 16 / 9 / 88

As the above table shows, the sample frame consists of 88 strata altogether. Households in each stratum of the sample frame are exclusively and exhaustively divided into PSUs. In urban areas, each city or town is divided into a number of enumeration blocks with welldefined boundaries and maps. Each enumeration block consists of about 200-250 households, and is taken to be a separate PSU. The list of enumeration blocks is updated every five years or so, with the list used for the PIHS having been modified on the basis of the Census of Establishments conducted in 1988. In rural areas, demarcation of PSUs has been done on the basis of the list of villages/mouzas/dehs published by the Population Census Organization based on the 1981 Census. Each of these villages/mouzas/dehs is taken to be a separate PSU. Altogether, the sample frame consists of approximately 18,000 urban and 43,000 rural PSUs.

SAMPLE SELECTION:

The PIHS sample comprised 4,800 households drawn from 300 PSUs throughout the country. Sample PSUs were divided equally between urban and rural areas, with at least two PSUs selected from each of the strata. Selection of PSUs from within each stratum was carried out using the probability proportional to estimated size method. In urban areas, estimates of the size of PSUs were based on the household count as found during the 1988 Census of Establishments. In rural areas, these estimates were based on the population count during the 1981 Census.

Once sample PSUs had been identified, a listing of all households residing in the PSU was made in all those PSUs where such a listing exercise had not been undertaken recently. Using systematic sampling with a random start, a short-list of 24 households was prepared for each PSU. Sixteen households from this list were selected to be interviewed from the PSU; every third household on the list was designated as a replacement household to be interviewed only if it was not possible to interview either of the two households immediately preceding it on the list.

As a result of replacing households that could not be interviewed because of non-responses, temporary absence, and other such reasons, the actual number of households interviewed during the survey - 4,794 - was very close to the planned sample size of 4,800 households. Moreover, following a pre-determined procedure for replacing households had the added advantage of minimizing any biases that may otherwise have arisen had field teams been allowed more discretion in choosing substitute households.

SAMPLE DESIGN EFFECTS:

The three-stage stratified sampling procedure outlined above has several advantages from the point of view of survey organization and implementation. Using this procedure ensures that all regions or strata deemed important are represented in the sample drawn for the survey. Picking clusters of households or PSUs in the various strata rather than directly drawing households randomly from throughout the country greatly reduces travel time and cost. Finally, selecting a fixed number of households in each PSU makes it easier to distribute the workload evenly amongst field teams. However, in using this procedure to select the sample for the survey, two important matters need to be given consideration: (a) sampling weights or raising factors have to be first calculated to get national estimates from the survey data; and (b) the standard errors for estimates obtained from the data need to be adjusted to take account for the use of this procedure.

Mode of data collection

Face-to-face [f2f]

Research instrument

The PIHS used three questionnaires: a household questionnaire, a community questionnaire, and a price questionnaire.

HOUSEHOLD QUESTIONNAIRE:

The PIHS questionnaire comprised 17 sections, each of which covered a separate aspect of household activity. The various sections of the household questionnaire were as follows: 1. HOUSEHOLD INFORMATION 2. HOUSING 3. EDUCATION 4. HEALTH 5. WAGE EMPLOYMENT 6. FAMILY LABOR 7. ENERGY 8. MIGRATION 9. FARMING AND LIVESTOCK 10. NON-FARM ENTERPRISE ACTIVITIES 11. NON-FOOD EXPENDITURES AND INVENTORY OF DURABLE GOODS 12. FOOD EXPENSES AND HOME PRODUCTION 13. MARRIAGE AND MATERNITY HISTORY 14. ANTHROPOMETRICS 15. CREDIT AND SAVINGS 16. TRANSFERS AND REMITTANCES 17. OTHER INCOME

The household questionnaire was designed to be administered in two visits to each sample household. Apart from avoiding the problem of interviewing household members in one long stretch, scheduling two visits also allowed the teams to improve the quality of the data collected.

During the first visit to the household (Round 1), the enumerators covered sections 1 to 8, and fixed a date with the designated respondents of the household for the second visit. During the second visit (Round 2), which was normally held two weeks after the first visit, the enumerators covered the remaining portion of the questionnaire and resolved any omissions or inconsistencies that were detected during data entry of information from the first part of the survey.

Since many of the sections of the questionnaire pertained specifically to female members of the household, female interviewers were included in conducting the survey. The household questionnaire was split into two parts (Male and Female). Sections such as SECTION 3: EDUCATION, which solicited information on all individual members of the household (male as well as female) were included in both parts of the questionnaire. Other sections such as SECTION 2: HOUSING and SECTION 12: FOOD EXPENSES AND HOME PRODUCTION , which collected data at the aggregate household level, were included in either the male questionnaire or the female questionnaire, depending upon which member of the household was more likely

Abstract

This research was conducted in Pakistan between January 2006 and December 2007. Data from 935 manufacturing and service sector registered establishments was analyzed.

The objective of the survey is to obtain feedback from enterprises in client countries on the state of the private sector as well as to help in building a panel of enterprise data that will make it possible to track changes in the business environment over time, thus allowing, for example, impact assessments of reforms. Through interviews with firms in the manufacturing and services sectors, the survey assesses the constraints to private sector growth and creates statistically significant business environment indicators that are comparable across countries.

The survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures. The questionnaire also assesses the survey respondents' opinions on what are the obstacles to firm growth and performance. The mode of data collection is face-to-face interviews.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Kind of data

Sample survey data [ssd]

Sampling procedure

Establishments were selected using stratified random sampling design. The survey covered manufacturing and services sectors and generated a large enough sample size for selected industries to conduct statistically robust analyses. With level of precision at a minimum 7.5 percent for 90 percent confidence intervals about estimates of population proportions and mean of log sales at the national, provincial and industry level.

The sampling frame was drawn from the 2005 Economic Census of Pakistan, conducted by Pakistan's Federal Bureau of Statistics (FBS). As the target population was formal, urban manufacturing and services establishments with more than 5 full-time employees, the census identified 583,329 manufacturing firms and 1,566,722 establishments in Wholesale/Retail trade & Restaurants.

In accordance with the size and make up of the economy, the manufacturing sector was stratified into five 2-digit Pakistan Standard Industrial Classification (PSIC) sectors: (i) food processing, (ii) textiles, apparel & leather, (iii) chemicals and products, (iv) metal and electric machinery, and (v) sports goods and handicrafts with a residual stratum based on the 14 largest cities from the four provinces of the country. Services establishments engaged in wholesale & retail trade, hotels & restaurants were grouped to constitute an independent stratum for each provincial capital.

Within each industry, the total sample size was distributed to the provincial/city sub-strata based on proportional allocation in order to be representative of the nation, the industry groups and the urban areas of each of the four provinces. Given the domination of smaller firms in sample frame, a sampling approach which oversampled larger firms was employed to ensure a sufficient number of large enterprise which otherwise might be underrepresented.

The specific steps involved: (i) extracting from the frame and dividing into activity/industry groups with selection made in proportion to each group's contribution to total industrial employment, (ii) allocating the establishments selected in to each industry group across the provinces/cities selected using a proportional allocation, and (iii) selecting the establishments for each province/city sub-stratum with a probability of selection which is inversely proportional to size (i.e. larger firms will be selected with a higher probability). Due to the oversampling of larger firms, weights were computed so that inferences about the population could be extrapolated from the sample.

The Pakistan Enterprise Survey 2007 sample was also designed to include up to 600 firms from the original sample of Pakistan ICS 2002. Out of a total of 846 establishments surveyed in 2002 (panel firms with location and other identifiers). The remaining firms were kept as potential replacements in case of non-response by an establishment of similar characteristics in the original panel sample. In the end, 402 firms were interviewed out of 795 firms contacted.

Mode of data collection

Face-to-face [f2f]

Research instrument

The current survey instruments are available: - Pakistan 2007 Manufacturing Sector Questionnaire; - Pakistan 2007 Services Sector Questionnaire.

The survey is fielded via two instruments in order to not ask questions that are irrelevant to specific types of firms, e.g. a question that relates to production and nonproduction workers should not be asked of a retail firm.

The survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures. Over 90% of the questions objectively ascertain characteristics of a country’s business environment. The remaining questions assess the survey respondents’ opinions on what are the obstacles to firm growth and performance.

Response rate

The field work involved a sample of almost 2700 firms with more than 2300 firms contacted in order to complete the survey of 1337 firms - 57 percent success rate. Of the 1000 non-successful contacts, about 45 percent were not located due to poor contact information and 25 percent refused to participate. Of the rest, 20 percent were closed and 10 percent were either non-responsive or produced non-usable data. For the non-panel sample, the response rate was slightly higher at 60 percent, but of the 612 nonresponding firms, 55 percent were not found due to insufficient contact information, 21 percent refused participation, 11 percent were non-usable and 13 percent were confirmed as closed.

Abstract

The 2012-13 Pakistan Demographic and Health Survey was undertaken to provide current and reliable data on fertility and family planning, childhood mortality, maternal and child health, women’s and children’s nutritional status, women’s empowerment, domestic violence, and knowledge of HIV/AIDS. The survey was designed with the broad objective of providing policymakers with information to monitor and evaluate programmatic interventions based on empirical evidence.

The specific objectives of the survey are to: • collect high-quality data on topics such as fertility levels and preferences, contraceptive use, maternal and child health, infant (and especially neonatal) mortality levels, awareness regarding HIV/AIDS, and other indicators related to the Millennium Development Goals and the country’s Poverty Reduction Strategy Paper • investigate factors that affect maternal and neonatal morbidity and mortality (i.e., antenatal, delivery, and postnatal care) • provide information to address the evaluation needs of health and family planning programs for evidence-based planning • provide guidelines to program managers and policymakers that will allow them to effectively plan and implement future interventions

Geographic coverage

National coverage

Analysis unit

• Household
• Ever married women age 15-49
• Ever married men age 15-49

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Design The primary objective of the 2012-13 PDHS is to provide reliable estimates of key fertility, family planning, maternal, and child health indicators at the national, provincial, and urban and rural levels. NIPS coordinated the design and selection of the sample with the Pakistan Bureau of Statistics. The sample for the 2012-13 PDHS represents the population of Pakistan excluding Azad Jammu and Kashmir, FATA, and restricted military and protected areas. The universe consists of all urban and rural areas of the four provinces of Pakistan and Gilgit Baltistan, defined as such in the 1998 Population Census. PBS developed the urban area frame. All urban cities and towns are divided into mutually exclusive, small areas, known as enumeration blocks, that were identifiable with maps. Each enumeration block consists of about 200 to 250 households on average, and blocks are further grouped into low-, middle-, and high-income categories. The urban area sampling frame consists of 26,543 enumeration blocks, updated through the economic census conducted in 2003. In rural areas, lists of villages/mouzas/dehs developed through the 1998 population census were used as the sample frame. In this frame, each village/mouza/deh is identifiable by its name. In Balochistan, Islamabad, and Gilgit Baltistan, urban areas were oversampled and proportions were adjusted by applying sampling weights during the analysis.

A sample size of 14,000 households was estimated to provide reasonable precision for the survey indicators. NIPS trained 43 PBS staff members to obtain fresh listings from 248 urban and 252 rural survey sample areas across the country. The household listing was carried out from August to December 2012.

The second stage of sampling involved selecting households. At each sampling point, 28 households were selected by applying a systematic sampling technique with a random start. This resulted in 14,000 households being selected (6,944 in urban areas and 7,056 in rural areas). The survey was carried out in a total of 498 areas. Two areas of Balochistan province (Punjgur and Dera Bugti) were dropped because of their deteriorating law and order situations. Overall, 24 areas (mostly in Balochistan) were replaced, mainly because of their adverse law and order situation.

Refer to Appendix B in the final report for details of sample design and implementation.

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2012-13 PDHS used four types of questionnaires: Household Questionnaire, Woman’s Questionnaire, Man’s Questionnaire, and Community Questionnaire. The contents of the Household, Woman’s, and Man’s Questionnaires were based on model questionnaires developed by the MEASURE DHS program. However, the questionnaires were modified, in consultation with a broad spectrum of research institutions, government departments, and local and international organizations, to reflect issues relevant to the Pakistani population, including migration status, family planning, domestic violence, HIV/AIDS, and maternal and child health. A series of questionnaire design meetings were organized by NIPS, and discussions from these meetings were used to finalize the survey questionnaires. The questionnaires were then translated into Urdu and Sindhi and pretested, after which they were further refined. The questionnaires were presented to the Technical Advisory Committee for final approval.

The Household Questionnaire was used to list the usual members and visitors in the selected households. Basic information was collected on the characteristics of each person listed, including age, sex, marital status, education, and relationship to the head of the household. Data on current school attendance, migration status, and survivorship of parents among those under age 18 were also collected. The questionnaire also provided the opportunity to identify ever-married women and men age 15-49 who were eligible for individual interviews and children age 0-5 eligible for anthropometry measurements. The Household Questionnaire collected information on characteristics of the dwelling unit as well, such as the source of drinking water; type of toilet facilities; type of cooking fuel; materials used for the floor, roof, and walls of the house; and ownership of durable goods, agricultural land, livestock/farm animals/poultry, and mosquito nets.

The Woman’s Questionnaire was used to collect information from ever-married women age 15-49 on the following topics: • Background characteristics (education, literacy, native tongue, marital status, etc.) • Reproductive history • Knowledge and use of family planning methods • Fertility preferences • Antenatal, delivery, and postnatal care • Breastfeeding and infant feeding practices • Vaccinations and childhood illnesses • Woman’s work and husband’s background characteristics • Infant and childhood mortality • Women’s decision making • Awareness about AIDS and other sexually transmitted infections • Other health issues (e.g., knowledge of tuberculosis and hepatitis, injection safety) • Domestic violence

Similarly, the Man’s Questionnaire, used to collect information from ever-married men age 15-49, covered the following topics: • Background characteristics • Knowledge and use of family planning methods • Fertility preferences • Employment and gender roles • Awareness about AIDS and other sexually transmitted infections • Other health issues

The Community Questionnaire, a brief form completed for each rural sample point, included questions about the availability of various types of health facilities and other services, particularly transportation, education, and communication facilities.

All elements of the PDHS data collection activities were pretested in June 2012. Three teams were formed for the pretest, each consisting of a supervisor, a male interviewer, and three female interviewers. One team worked in the Sukkur and Khairpur districts in the province of Sindh, another in the Peshawar and Charsadda districts in Khyber Pakhtunkhwa, and the third in the district of Rawalpindi in Punjab. Each team covered one rural and one urban non-sample area.

Cleaning operations

The processing of the 2012-13 PDHS data began simultaneously with the fieldwork. Completed questionnaires were edited and data entry was carried out immediately in the field by the field editors. The data were uploaded on the same day to enable retrieval in the central office at NIPS in Islamabad, and the Internet File Streaming System was used to transfer data from the field to the central office. The completed questionnaires were then returned periodically from the field to the NIPS office in Islamabad through a courier service, where the data were again edited and entered by data processing personnel specially trained for this task. Thus, all data were entered twice for 100 percent verification. Data were entered using the CSPro computer package. The concurrent processing of the data offered a distinct advantage because of the assurance that the data were error-free and authentic. Moreover, the double entry of data enabled easy identification of errors and inconsistencies, which were resolved via comparisons with the paper questionnaire entries. The secondary editing of the data was completed in the first week of May 2013.

As noted, the PDHS used the CAFE system in the field for the first time. This application was developed and fully tested before teams were deployed in the field. Field editors were selected after careful screening from among the participants who attended the main training exercise. Seven-day training was arranged for field editors so that each editor could enter a sample cluster’s data under the supervision of NIPS senior staff, which enabled a better understanding of the CAFE system. The system was deemed efficient in capturing data immediately in the field and providing immediate feedback to the field teams. Early transfer of data back to the central office enabled the generation of field check tables on a regular basis, an efficient tool for monitoring the fieldwork.

Response rate

A total of 13,944 households were selected for the sample, of which

This collection contains two datasets: one, data used in TI-City model to predict future urban expansion in Accra, Ghana; and two, residential electricity consumption data used to map intra-urban living standards in Karachi, Pakistan. The TI-City model data are ASCII files of infrastructure and amenities that affect location decisions of households and developers. The residential electricity consumption data consist of average kilowatt hours (kw/h) of electricity consumed per month by ~ 2 million households in Karachi. The electricity consumption data is aggregated into 30m grid cells (count = 193050), with centroids and consumption values provided. The values of the points (centroids), captured under the field "Avg_Avg_Cs", represents the median of average monthly consumption of households within the 30m grid cells.

Our project addresses a critical gap in social research methodology that has important implications for combating urban poverty and promoting sustainable development in low and middle-income countries. Simply put, we're creating a low-cost tool for gathering critical information about urban population dynamics in cities experiencing rapid spatial-demographic and socioeconomic change. Such information is vital to the success of urban planning and development initiatives, as well as disaster relief efforts. By improving the information base of the actors involved in such activities we aim to improve the lives of urban dwellers across the developing world, particularly the poorest and most vulnerable. The key output for the project will be a freely available 'City Sampling Toolkit' that provides detailed instructions and opensource software tools for replicating the approach at various spatial scales.

Our research is motivated by the growing recognition that cities are critical arenas for action in global efforts to tackle poverty and transition towards more environmentally sustainable economic growth. Between now and 2050 the global urban population is projected to grow by over 2 billion, with the overwhelming majority of this growth taking place in low and middle-income countries in Africa and Asia. Developing evidence-based policies for managing this growth is an urgent task. As UN Secretary General Ban Ki Moon has observed: "Cities are increasingly the home of humanity. They are central to climate action, global prosperity, peace and human rights...To transform our world, we must transform its cities."

Unfortunately, even basic data about urban populations are lacking in many of the fastest growing cities of the world. Existing methods for gathering vital information, including censuses and sample surveys, have critical limitations in urban areas experiencing rapid change. And 'big data' approaches are not an adequate substitute for representative population data when it comes to urban planning and policymaking. We will overcome these limitations through a combination of conceptual innovation and creative integration of novel tools and techniques that have been developed for sampling, surveying and estimating the characteristics of populations that are difficult to enumerate. This, in turn, will help us capture the large (and sometimes uniquely vulnerable) 'hidden populations' in cities missed by traditional approaches.

By using freely available satellite imagery, we can get an idea of the current shape of a rapidly changing city and create a 'sampling frame' from which we then identify respondents for our survey. Importantly, and in contrast with previous approaches, we aren't simply going to count official city residents. We are interested in understanding the characteristics of the actually present population, including recent migrants, temporary residents, and those living in informal or illegal settlements, who are often not considered formal residents in official enumeration exercises. In other words, our 'inclusion criterion' for the survey exercise is presence not residence. By adopting this approach, we hope to capture a more accurate picture of city populations. We will also limit the length of our survey questionnaire to maximise responses and then use novel statistical techniques to reconstruct a rich statistical portrait that reflects a wide range of demographic and socioeconomic information.

We will pilot our methodology in a city in Pakistan, which recently completed a national census exercise that has generated some controversy with regard to the accuracy of urban population counts. To our knowledge this would be the first project ever to pilot and validate a new sampling and survey methodology at the city scale in a developing country.

Abstract

A primary objective of the national Time Use Survey in Pakistan is to account for the 24 hours time in term of the full spectrum of activities carried out during the duration. The objectives of the survey are specified as under:- - To profile the quantum and distribution of paid/unpaid work as a means to infer policy/programme implications from the perspective of gender equity. - To collect and analyze the time use pattern of the individuals in order to help draw inferences for employment and welfare programmes. - To collect and analyze the comprehensive information about the time spent by people on marketed and non-marketed economic activities covered under the 1993-SNA, non-marketed non-SNA activities within the General Production Boundary and personal care and related activities that cannot be delegated to others. - To use the data in generating more reliable estimates on work force.

Geographic coverage

The survey covers all urban and rural areas of the four provinces of Pakistan defined as such by 1998 Population Census excluding Federally Administered Tribal Areas (FATA) and certain administrative areas of NWFP. The population of geographic areas excluded from the survey constitutes about 2 percent of the total population as enumerated in 1998 Population Census. The population excluded is located in difficult terrain and its enumeration through personal interview is not possible within the given constraints of time, access and cost.

Analysis unit

Households Individuals

Universe

The universe consists of all urban and rural areas of the four provinces of Pakistan, defined as such by Population Census 1998, excluding FATA & Military Restricted Areas. The population of excluded area constitutes about 3% of the total population and is located in different terrain.

Sampling procedure

Sampling Frame Federal Bureau of Statistics has developed its own sampling frame for all urban areas of the country. Each city/town has been divided into a number of enumeration blocks. Each enumeration block consists of 200-250 households on the average with well-defined boundaries and maps. The sampling frame i.e. lists of enumeration blocks as up-dated through Economic Census 2003-04 and the lists of villages/mouzas/dehs published by Population Census Organization as a result of 1998 Population Census have been taken as sampling frame. Enumeration blocks and villages are considered as primary sampling unites (PSUs) for urban and rural domain respectively.

Stratification a) Urban Domain i) Large Sized Cities Karachi, Lahore, Gujranwala, Faisalabad, Rawalpindi, Multan, Sialkot, Sargodha, Bahawapur, Hyderabad, Sukkur, Peshawar, Quetta and Islamabad are considered as large sized cities. Each of these cities constitutes a separate stratum which is further sub-stratified according to low, middle, high income groups based on the information collected in respect of each enumeration block at the time of demarcation/up-dating of urban area sampling frame. ii) Remaining urban areas After excluding the population of large sized cities from the population of respective administrative division, the remaining urban population of administrative division of four provinces is grouped together to form a stratum called other urban. Thus ex-division in remaining urban areas in the four provinces constitutes a stratum. b) Rural Domain In rural domain, each administrative district in the Punjab, Sindh and NWF Provinces is considered as independent and explicit stratum whereas, in Balochistan, each administrative division constitutes a stratum.

Sample size and its Allocation Keeping in view the resources available, a sample size of 19600 sample households has been considered appropriate to provide estimates of key characteristics at the desired level. The entire sample of households (SSUs) has been drawn from 1388 Primary Sampling Units (PSUs) out of which 652 are urban and 736 are rural. In order to control seasonal variation etc. sample has been distributed evenly over four quarters. This has facilitated to capture the variation due to any seasonal activity as urban population is more heterogeneous therefore, a higher proportion of sample size has been allocated to urban domain. Similarly NWFP and Balochistan being the smaller province, have been assigned higher proportion of sample in order to get reliable estimates. After fixing the sample size at provincial level, further distribution of sample PSUs to different strata in rural and urban domains in each province has been made proportionately.

Sample Design A three-stage stratified sample design has been adopted for the survey. Sample Selection Procedure a) Selection of Primary Sampling Unites (PSUs) Enumeration blocks in urban domain and mouzas/dehs/villages in rural domain are taken as primary sampling unites (PSUs). In the urban domain, sample PSUs from each ultimate stratum/sub-stratum is selected with probability proportional to size (PPS) method of sampling scheme. In urban domain, the number of households in enumeration block as up-dated through Economic Census 2003-04 and population of 1998 Census for each village/mouza/deh are considered as measure of size. b) Section of Secondary Sampling Units (SSUs) Households within sample PSUs are taken as secondary sampling unites (SSUs). A specified number of households i.e. 12 from each urban sample PSU and 16 from each rural sample PSU are selected with equal probability using systematic sampling technique with a random start. Different households are selected in each quarter. c) Selection of Third Stage Sampling Units i.e. Individuals/Persons (TSUs) From the sample households, individuals/persons aged 10+ years within each sample households (SSUs) have been taken as third stage sampling units (TSUs). Two individuals aged 10 years and above among the eligible individuals/persons from each sample household have been interviewed using a selection grid.The grid and selection steps are detailed on p13 of the survey report available under external resources.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire has been framed in the light of contemporary precedents and practices in vogue in the developing countries. The recommendations of Gender Responsive Budgeting Initiatives (GRBI) expert who visited Pakistan in June 2006 have been taken into account. Further, the advice of local experts hailing both from data producing and using agencies has also been considered. Survey Questionnaire and Manual of Instructions, for the Supervisors & Enumerators, was finalized jointly by Federal Bureau of Statistics and GRBI Project staff. The questionnaire was also pre-tested and reviewed accordingly. The questionnaire adopted for the survey is given at Annexure-A. All the households selected in the sample stand interviewed. Diary part of the questionnaire is filled-in from two respondents selected from each of the enumerated households. The questionnaire consists of the following six parts. Section-1: Identification of the area, respondents, detail of field visits and staff entrusted with supervision, editing and coding. Section-2: Detailed information about the socio-economic and demographic particulars of the selected households and individuals. Some of the important household characteristics i.e. ownership status and type of the household, earthquake damage, household items, sources of energy, drinking water, transport, health & education facilities, sources of income, monthly income, age and sex composition of the population. Section-3: Demographic detail such as age, sex, marital status, educational level, having children, employment status, source of income etc. of the selected respondent of that household Section-4: Comprised of diary to record the activities performed by the first selected respondent through the 24 hours period between 4.00 a.m. of the day preceding the day of interview and 3.00 a.m. on the day of the interview. Section-5 and 6 pertain to the second selected respondent of the selected household. The diary which is the core instrument of the time use study is divided into forty eight half-hour slots. An open ended question about the activities performed during the thirty minutes was asked from the respondent. Provision for minimum of recording three activities through half hour slot was made. In case of reporting more than one activity, the respondent was probed whether these activities were carried out simultaneously or one after the other. Similarly, the two locations of performing the activities were also investigated in the diary part of the questionnaire. The activities recorded in the diary are then coded by the field enumerator according to the activity classification given at Annex-B.

Cleaning operations

Soon after data collection, the field supervisors manually clean, edit and check the filled in questionnaire and refer back to field where necessary. This does not take much time since most of the manual editing is done in the field. Further editing is done by the subject matter section at the Headquarter. Also during data entry, further editing of error identified by applying computer edit checks is done. In edit checks, data ranges in numerical values are used to eliminate erroneous data as a result of mistakes made during coding. Thus, the survey records are edited and corrected through a series of computer processing stages.